{"id":"W4401281194","doi":"10.1016/j.scitotenv.2024.175256","title":"Improving monitoring network design to detect leaks at hazardous facilities: Lessons from a CO2 storage site","year":2024,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"CO2 Sequestration and Geologic Interactions","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Korea Environmental Industry and Technology Institute; National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; Institute for Korea Spent Nuclear Fuel","keywords":"Hazardous waste; Hydrogeology; Calibration; Computer science; Probabilistic logic; Latin hypercube sampling; Uncertainty quantification; Fault detection and isolation; Reliability engineering; Environmental science; Data mining; Engineering; Monte Carlo method; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007490134,0.0001491703,0.0001087919,0.00002400079,0.000726843,0.00009920459,0.0006162644,0.00003464209,0.001736134],"category_scores_gemma":[0.00006232218,0.00008958751,0.00009121207,0.0002988288,0.0008626985,0.0002253889,0.001034015,0.0001897448,0.001037778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007336949,"about_ca_system_score_gemma":0.0000298021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001288212,"about_ca_topic_score_gemma":0.00003467919,"domain_scores_codex":[0.9982958,0.00009843338,0.0002090934,0.0004175265,0.0006049474,0.0003741495],"domain_scores_gemma":[0.9990974,0.000173866,0.00006799073,0.000557212,0.000003083369,0.0001004258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001306407,0.000009212075,0.00003426096,0.000001554071,0.000006105263,0.000002160077,0.001656692,0.7404691,0.2531516,0.00002480584,0.0003077043,0.00432373],"study_design_scores_gemma":[0.0004291483,0.0007565513,0.07513629,0.0002831721,0.0001877649,0.0001207271,0.002863516,0.3222014,0.5769103,0.005307862,0.01455083,0.001252425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9841596,0.0001291206,0.01092358,0.002227549,0.0009555022,0.0004445206,0.00002675175,0.00004883069,0.001084489],"genre_scores_gemma":[0.988846,0.00001296251,0.001869593,0.00003350952,0.00008412793,0.00003660417,4.084184e-7,0.000008130031,0.009108684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4182677,"threshold_uncertainty_score":0.99974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02208214875247656,"score_gpt":0.2423931886302083,"score_spread":0.2203110398777318,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}